摘要翻译：
本文采用分段线性趋势模型，对新冠肺炎累计确诊病例和死亡病例的轨迹（对数尺度）进行了建模。该模型通过变点自然地捕捉了流行病增长率的相变，并因其半参数性质而具有很好的解释性。在方法学方面，我们提出了新的自归一化(SN)技术（Shao,2010）来检验和估计非平稳时间序列线性趋势中的单个变化点。我们进一步将基于SN的变点测试与NOT算法（Baranowski et al.，2019)相结合，实现多变点估计。利用所提出的方法，我们分析了30个主要国家的累计新冠肺炎病例和死亡轨迹，并发现了有趣的模式，这些模式对不同国家应对疫情的有效性有潜在的相关影响。此外，基于变点检测算法和灵活的外推函数，我们设计了一个简单的新冠肺炎两阶段预测方案，并证明了该方案在预测美国累计死亡人数方面的良好性能。
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英文标题：
《Time Series Analysis of COVID-19 Infection Curve: A Change-Point
  Perspective》
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作者：
Feiyu Jiang, Zifeng Zhao, Xiaofeng Shao
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最新提交年份：
2020
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分类信息：

一级分类：Economics        经济学
二级分类：Econometrics        计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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一级分类：Physics        物理学
二级分类：Physics and Society        物理学与社会
分类描述：Structure, dynamics and collective behavior of societies and groups (human or otherwise). Quantitative analysis of social networks and other complex networks. Physics and engineering of infrastructure and systems of broad societal impact (e.g., energy grids, transportation networks).
社会和团体（人类或其他）的结构、动态和集体行为。社会网络和其他复杂网络的定量分析。具有广泛社会影响的基础设施和系统（如能源网、运输网络）的物理和工程。
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一级分类：Statistics        统计学
二级分类：Applications        应用程序
分类描述：Biology, Education, Epidemiology, Engineering, Environmental Sciences, Medical, Physical Sciences, Quality Control, Social Sciences
生物学，教育学，流行病学，工程学，环境科学，医学，物理科学，质量控制，社会科学
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英文摘要：
  In this paper, we model the trajectory of the cumulative confirmed cases and deaths of COVID-19 (in log scale) via a piecewise linear trend model. The model naturally captures the phase transitions of the epidemic growth rate via change-points and further enjoys great interpretability due to its semiparametric nature. On the methodological front, we advance the nascent self-normalization (SN) technique (Shao, 2010) to testing and estimation of a single change-point in the linear trend of a nonstationary time series. We further combine the SN-based change-point test with the NOT algorithm (Baranowski et al., 2019) to achieve multiple change-point estimation. Using the proposed method, we analyze the trajectory of the cumulative COVID-19 cases and deaths for 30 major countries and discover interesting patterns with potentially relevant implications for effectiveness of the pandemic responses by different countries. Furthermore, based on the change-point detection algorithm and a flexible extrapolation function, we design a simple two-stage forecasting scheme for COVID-19 and demonstrate its promising performance in predicting cumulative deaths in the U.S.
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PDF链接：
https://arxiv.org/pdf/2007.04553